Convolutional Neural Network Architecture for Recovering Watermark Synchronization

TL;DR

This paper introduces a CNN-based architecture that improves watermark synchronization by compensating for geometric distortions like scaling and translation, enhancing copyright protection in real-time content sharing.

Contribution

A novel CNN-based template architecture that detects and corrects geometric distortions, enabling robust watermark decoding despite common transformations.

Findings

Effective recovery of original images after geometric distortions

Improved robustness of watermark decoding in real-time applications

Demonstrated success in compensating for scaling and translation distortions

Abstract

Since real-time contents can be captured and downloaded very easily, copyright infringement has become a serious problem. In order to reduce the loss caused by copyright infringement, copyright owners insert a watermark in the content to protect the copyright using illegal distribution route tracking and copyright authentication. However, whereas many existing watermarking techniques are robust to signal distortion such as compression, they are vulnerable to geometric distortion that causes synchronization errors. In particular, capturing real-time content in Internet browsers and smartphone applications is problematic because geometric distortion such as scaling and translation frequently occurs. In this paper, we propose a convolutional neural network-based template architecture that compensates for the disadvantages of existing watermarking techniques that are vulnerable to geometric distortion. The proposed template consists of a template generation network, a template extraction network, and a template matching network. The template generation network generates a template in the form of noise and the template is inserted into certain pre-defined spatial locations of the image. The extraction network detects spatial locations where the template is inserted in the image. Finally, the template matching network estimates the parameters of the geometric distortion by comparing the shape of spatial locations where the template was inserted with the locations where the template was detected. It is possible to recover an image in its original geometrical form using the estimated parameters, and as a result, watermarks applied using existing watermarking techniques that are vulnerable to geometric distortion can be decoded normally.